Space-Time Multiscale-Multiphysics Homogenization Methods for Heterogeneous Materials

We present a unified, homogenization framework for computational analysis of heterogeneous materials consisting of multiple length scales, multiple time scales and coupled-multiple physics. The research efforts also addresses the technological issues associated with modeling the morphological details of microstructures with randomly distributed inclusions. The Random Sequential Adsorption (RSA) algorithm is improved to accurately and effectively model the morphological details of materials with randomly distributed inclusions. The proposed algorithm is more robust; computational efficient and versatile in comparison to the existing methods. A temporal homogenization scheme is developed and integrated with the previously developed spatial homogenization theory for fatigue life analysis of heterogeneous materials. The unified space-time multiscale homogenization model is validated for fatigue life prediction of elevated temperature Ceramic Matrix Composites (CMCs). In the final phase of the research a mathematical model for coupled moisture diffusion-mechanical deformation is developed. This model is integrated with the spatial homogenization framework to analyze problems consisting of multiple length scales and coupled-multiple physics. The unified multiscale-multiphysics model is validated for evaluating the degradation of physical and mechanical properties of short glass fiber and carbon fiber filled thermoplastic material systems

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

Beyond the here and now : herbivore ecology in a spatial-temporal context

Ecological phenomena at a certain location often not only depend on the current characteristics of that location itself, but also on characteristics of the landscape surrounding the site or influences from the past. In other words, in order to be able to understand ecological processes here and now, we often need information that is beyond here and now. This thesis investigates the role of such spatial-temporal context on the relationships between species and their environment. The focus is on large mammalian herbivores, an in particular the factors determining the movement and distribution of African elephants (Loxodonta africana) in Kruger National Park (KNP), South Africa. This thesis covers four main themes. First, it is shown that spatial-temporal variation in the biotic and abiotic environment creates predictable seasonal and daily cyclic patterns in the distribution of elephants in KNP. Then, herbivores are shown not only to respond to environmental heterogeneity: they also can create distinct spatial patterning in savanna vegetation when foraging initiates a positive plant-herbivore feedback and when the foraging process is spatially explicit. This leads to specific sites being revisited often, and therefore the patterns of site revisitation by elephants in KNP are studied, mainly in relation to surface water availability and vegetation characteristics. Lastly, this thesis focuses on the scale-dependent response of organisms to environmental heterogeneity, showing the consequences of a mismatch in the scale of analysis on statistical inference, and then focusing on finding the appropriate spatial scales to analyse and predict the spatial distribution of elephants in KNP. Together, the chapters presented in this thesis highlight the importance of explicitly considering the scale and context dependency of species-environment relationships, and demonstrate methods to take issues of spatial-temporal scale and context into account. These methods have the potential to increase our understanding of ecological phenomena and therefore may lead to better management of natural resources. <br/

Gas hydrate technology: state of the art and future possibilities for Europe

Interest in natural gas hydrates has been steadily increasing over the last few decades, with the understanding that exploitation of this abundant unconventional source may help meet the ever-increasing energy demand and assist in reduction of CO2 emission (by replacing coal). Unfortunately, conventional technologies for oil and gas exploitation are not fully appropriate for the specific exploitation of gas hydrate. Consequently, the technology chain, from exploration through production to monitoring, needs to be further developed and adapted to the specific properties and conditions associated with gas hydrates, in order to allow for a commercially and environmentally sound extraction of gas from gas hydrate deposits. Various academic groups and companies within the European region have been heavily involved in theoretical and applied research of gas hydrate for more than a decade. To demonstrate this, Fig. 1.1 shows a selection of leading European institutes that are actively involved in gas hydrate research. A significant number of these institutes have been strongly involved in recent worldwide exploitation of gas hydrate, which are shown in Fig. 1.2 and summarized in Table 1.1. Despite the state of knowledge, no field trials have been carried out so far in European waters. MIGRATE (COST action ES1405) aims to pool together expertise of a large number of European research groups and industrial players to advance gas-hydrate related activity with the ultimate goal of preparing the setting for a field production test in European waters. This MIGRATE report presents an overview of current technologies related to gas hydrate exploration (Chapter 2), production (Chapter 3) and monitoring (Chapter 4), with an emphasis on European activity. This requires covering various activities within different disciplines, all of which contribute to the technology development needed for future cost-effective gas production. The report points out future research and work areas (Chapter 5) that would bridge existing knowledge gaps, through multinational collaboration and interdisciplinary approaches

Gradient metasurfaces: a review of fundamentals and applications

In the wake of intense research on metamaterials the two-dimensional analogue, known as metasurfaces, has attracted progressively increasing attention in recent years due to the ease of fabrication and smaller insertion losses, while enabling an unprecedented control over spatial distributions of transmitted and reflected optical fields. Metasurfaces represent optically thin planar arrays of resonant subwavelength elements that can be arranged in a strictly or quasi periodic fashion, or even in an aperiodic manner, depending on targeted optical wavefronts to be molded with their help. This paper reviews a broad subclass of metasurfaces, viz. gradient metasurfaces, which are devised to exhibit spatially varying optical responses resulting in spatially varying amplitudes, phases and polarizations of scattered fields. Starting with introducing the concept of gradient metasurfaces, we present classification of different metasurfaces from the viewpoint of their responses, differentiating electrical-dipole, geometric, reflective and Huygens' metasurfaces. The fundamental building blocks essential for the realization of metasurfaces are then discussed in order to elucidate the underlying physics of various physical realizations of both plasmonic and purely dielectric metasurfaces. We then overview the main applications of gradient metasurfaces, including waveplates, flat lenses, spiral phase plates, broadband absorbers, color printing, holograms, polarimeters and surface wave couplers. The review is terminated with a short section on recently developed nonlinear metasurfaces, followed by the outlook presenting our view on possible future developments and perspectives for future applications.Comment: Accepted for publication in Reports on Progress in Physic

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Marshall Space Flight Center Faculty Fellowship Program

The 2017 Marshall Faculty Fellowship Program involved 21 faculty in the laboratories and departments at Marshall Space Flight Center. These faculty engineers and scientists worked with NASA collaborators on NASA projects, bringing new perspectives and solutions to bear. This Technical Memorandum is a compilation of the research reports of the 2017 Marshall Faculty Fellowship program, along with the Program Announcement (Appendix A) and the Program Description (Appendix B). The research affected the following six areas: (1) Materials (2) Propulsion (3) Instrumentation (4) Spacecraft systems (5) Vehicle systems (6) Space science The materials investigations included composite structures, printing electronic circuits, degradation of materials by energetic particles, friction stir welding, Martian and Lunar regolith for in-situ construction, and polymers for additive manufacturing. Propulsion studies were completed on electric sails and low-power arcjets for use with green propellants. Instrumentation research involved heat pipes, neutrino detectors, and remote sensing. Spacecraft systems research was conducted on wireless technologies, layered pressure vessels, and two-phase flow. Vehicle systems studies were performed on life support-biofilm buildup and landing systems. In the space science area, the excitation of electromagnetic ion-cyclotron waves observed by the Magnetospheric Multiscale Mission provided insight regarding the propagation of these waves. Our goal is to continue the Marshall Faculty Fellowship Program funded by Center internal project offices. Faculty Fellows in this 2017 program represented the following minority-serving institutions: Alabama A&M University and Oglala Lakota College

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data